{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#  Python数据分析之展示-2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**导入**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.rcParams[\"font.sans-serif\"] = [\"SimHei\"]\n",
    "plt.rcParams[\"axes.unicode_minus\"] = False #解决中文乱码等问题"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_excel(r\"C:\\Users\\Lovetianyi\\Desktop\\python\\作业5\\movie_data3.xlsx\",index_col = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>年代</th>\n",
       "      <th>产地</th>\n",
       "      <th>名字</th>\n",
       "      <th>投票人数</th>\n",
       "      <th>类型</th>\n",
       "      <th>上映时间</th>\n",
       "      <th>时长</th>\n",
       "      <th>评分</th>\n",
       "      <th>首映地点</th>\n",
       "      <th>评分等级</th>\n",
       "      <th>热门程度</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1994</td>\n",
       "      <td>美国</td>\n",
       "      <td>肖申克的救赎</td>\n",
       "      <td>692795</td>\n",
       "      <td>剧情/犯罪</td>\n",
       "      <td>1994-09-10 00:00:00</td>\n",
       "      <td>142</td>\n",
       "      <td>9.6</td>\n",
       "      <td>多伦多电影节</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1957</td>\n",
       "      <td>美国</td>\n",
       "      <td>控方证人</td>\n",
       "      <td>42995</td>\n",
       "      <td>剧情/悬疑/犯罪</td>\n",
       "      <td>1957-12-17 00:00:00</td>\n",
       "      <td>116</td>\n",
       "      <td>9.5</td>\n",
       "      <td>美国</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1997</td>\n",
       "      <td>意大利</td>\n",
       "      <td>美丽人生</td>\n",
       "      <td>327855</td>\n",
       "      <td>剧情/喜剧/爱情</td>\n",
       "      <td>1997-12-20 00:00:00</td>\n",
       "      <td>116</td>\n",
       "      <td>9.5</td>\n",
       "      <td>意大利</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1994</td>\n",
       "      <td>美国</td>\n",
       "      <td>阿甘正传</td>\n",
       "      <td>580897</td>\n",
       "      <td>剧情/爱情</td>\n",
       "      <td>1994-06-23 00:00:00</td>\n",
       "      <td>142</td>\n",
       "      <td>9.4</td>\n",
       "      <td>洛杉矶首映</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1993</td>\n",
       "      <td>中国大陆</td>\n",
       "      <td>霸王别姬</td>\n",
       "      <td>478523</td>\n",
       "      <td>剧情/爱情/同性</td>\n",
       "      <td>1993-01-01 00:00:00</td>\n",
       "      <td>171</td>\n",
       "      <td>9.4</td>\n",
       "      <td>香港</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     年代    产地      名字    投票人数        类型                 上映时间   时长   评分  \\\n",
       "0  1994    美国  肖申克的救赎  692795     剧情/犯罪  1994-09-10 00:00:00  142  9.6   \n",
       "1  1957    美国    控方证人   42995  剧情/悬疑/犯罪  1957-12-17 00:00:00  116  9.5   \n",
       "2  1997   意大利   美丽人生   327855  剧情/喜剧/爱情  1997-12-20 00:00:00  116  9.5   \n",
       "3  1994    美国    阿甘正传  580897     剧情/爱情  1994-06-23 00:00:00  142  9.4   \n",
       "4  1993  中国大陆    霸王别姬  478523  剧情/爱情/同性  1993-01-01 00:00:00  171  9.4   \n",
       "\n",
       "     首映地点 评分等级 热门程度  \n",
       "0  多伦多电影节    A    A  \n",
       "1      美国    A    A  \n",
       "2     意大利    A    A  \n",
       "3   洛杉矶首映    A    A  \n",
       "4      香港    A    A  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 双轴图的画法 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.stats import norm #获取正态分布密度函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (10,8))\n",
    "ax1 = fig.add_subplot(111) #确认子图\n",
    "n,bins,patches = ax1.hist(df[\"评分\"],bins = 100, color = 'm') #bins默认是10\n",
    "\n",
    "ax1.set_ylabel(\"电影数量\",fontsize = 15)\n",
    "ax1.set_xlabel(\"评分\",fontsize = 15)\n",
    "ax1.set_title(\"频率分布图\",fontsize = 20)\n",
    "\n",
    "#准备拟合\n",
    "y = norm.pdf(bins,df[\"评分\"].mean(),df[\"评分\"].std()) #bins,mu,sigma\n",
    "ax2 = ax1.twinx() #双轴\n",
    "ax2.plot(bins,y,\"b--\")\n",
    "ax2.set_ylabel(\"概率分布\",fontsize = 15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.根据电影时长和电影评分绘制散点图 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "用两组数据构成多个坐标点，考察坐标点的分布，判断两变量之间是否存在某种关联或总结坐标点的分布模式。散点图将序列显示为一组点。值由点在图表中的位置表示。类别由图表中的不同标记表示。散点图通常用于比较跨类别的聚合数据。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = df[\"时长\"][::100]\n",
    "y = df[\"评分\"][::100] #解决数据冗杂的问题\n",
    "\n",
    "plt.figure(figsize = (10,6))\n",
    "plt.scatter(x,y,color = 'c',marker = 'p',label = \"评分\")\n",
    "plt.legend() #图例\n",
    "plt.title(\"电影时长与评分散点图\",fontsize = 20)\n",
    "plt.xlabel(\"时长\",fontsize = 18)\n",
    "plt.ylabel(\"评分\",fontsize = 18)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**由于我们的数据量过大，所以画出来的图非常冗杂**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**可以发现，大部分的电影时长还是集中在100附近，评分大多在7分左右**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **marker属性**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "设置散点的形状\n",
    "\n",
    "|**marker**|**description**|**描述**|\n",
    "|-|-|-|\n",
    "|\".\"|point|点|\n",
    "|\",\"|pixel|像素|\n",
    "|\"o\"|circle|圈|\n",
    "|\"v\"|triangle_down|倒三角形|\n",
    "|\"^\"|triangle_up|正三角形|\n",
    "|\"<\"|triangle_left|左三角形|\n",
    "|\">\"|triangle_right|右三角形|\n",
    "|\"1\"|tri_down|tri_down|\n",
    "|\"2\"|tri_up|tri_up|\n",
    "|\"3\"|tri_left|tri_left|\n",
    "|\"4\"|tri_right|tri_right|\n",
    "|\"8\"|octagon|八角形|\n",
    "|\"s\"|square|正方形|\n",
    "|\"p\"|pentagon|五角|\n",
    "|\"\\*\"|star|星星|\n",
    "|\"h\"|hexagon1|六角1|\n",
    "|\"H\"|hexagon2|六角2|\n",
    "|\"+\"|plus|加号|\n",
    "|\"x\"|x|x号|\n",
    "|\"D\"|diamond|钻石|\n",
    "|\"d\"|thin_diamon|细钻|\n",
    "|\"\\|\"|vline|v线|\n",
    "|\"\\_\"|hline|H线|"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6.绘制各个地区的评分箱型图"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "箱型图（Box-plot）又称为盒须图，盒式图或箱型图，是一种用作显示一组数据分散情况资料的统计图。因形状如箱子而得名。在各种领域中也经常被使用，常见于品质管理。它主要用于反映原始数据分布的特征，还可以进行多组数据分布特征的比较。箱线图的绘制方法是：先找出一组数据的中位数，两个四分位数，上下边缘线；然后，连接两个四分位数画出箱子；再将上下边缘线与箱子相连接，中位数在箱子中间。\n",
    "\n",
    "![test](375d3e018a4a35cde9d3cb178ce43ce.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**一般计算过程**\n",
    "\n",
    "（ 1 ）计算上四分位数（ Q3 ），中位数，下四分位数（ Q1 ）\n",
    "\n",
    "（ 2 ）计算上四分位数和下四分位数之间的差值，即四分位数差（IQR, interquartile range）Q3-Q1\n",
    "\n",
    "（ 3 ）绘制箱线图的上下范围，上限为上四分位数，下限为下四分位数。在箱子内部中位数的位置绘制横线\n",
    "\n",
    "（ 4 ）大于上四分位数1.5倍四分位数差的值，或者小于下四分位数1.5倍四分位数差的值，划为异常值（outliers）\n",
    "\n",
    "（ 5 ）异常值之外，最靠近上边缘和下边缘的两个值处，画横线，作为箱线图的触须\n",
    "\n",
    "（ 6 ）极端异常值，即超出四分位数差3倍距离的异常值，用实心点表示；较为温和的异常值，即处于1.5倍-3倍四分位数差之间的异常值，用空心点表示\n",
    "\n",
    "（ 7 ）为箱线图添加名称，数轴等"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**参数详解**\n",
    "\n",
    "```python\n",
    "plt.boxplot(x,notch=None,sym=None,vert=None,\n",
    "    whis=None,positions=None,widths=None,\n",
    "    patch_artist=None,meanline=None,showmeans=None,\n",
    "    showcaps=None,showbox=None,showfliers=None,\n",
    "    boxprops=None,labels=None,flierprops=None,\n",
    "    medianprops=None,meanprops=None,\n",
    "    capprops=None,whiskerprops=None,)\n",
    "```\n",
    "x: 指定要绘制箱线图的数据；\n",
    "\n",
    "notch: 是否是凹口的形式展现箱线图，默认非凹口；\n",
    "\n",
    "sym: 指定异常点的形状，默认为+号显示；\n",
    "\n",
    "vert: 是否需要将箱线图垂直摆放，默认垂直摆放；\n",
    "\n",
    "whis: 指定上下须与上下四分位的距离，默认为为1.5倍的四分位差；\n",
    "\n",
    "positions: 指定箱线图的位置，默认为[0,1,2...]；\n",
    "\n",
    "widths: 指定箱线图的宽度，默认为0.5；\n",
    "\n",
    "patch_artist: 是否填充箱体的颜色；\n",
    "\n",
    "meanline:是否用线的形式表示均值，默认用点来表示；\n",
    "\n",
    "showmeans: 是否显示均值，默认不显示；\n",
    "\n",
    "showcaps: 是否显示箱线图顶端和末端的两条线，默认显示；\n",
    "\n",
    "showbox: 是否显示箱线图的箱体，默认显示；\n",
    "\n",
    "showfliers: 是否显示异常值，默认显示；\n",
    "\n",
    "boxprops: 设置箱体的属性，如边框色，填充色等；\n",
    "\n",
    "labels: 为箱线图添加标签，类似于图例的作用；\n",
    "\n",
    "filerprops: 设置异常值的属性，如异常点的形状、大小、填充色等；\n",
    "\n",
    "medainprops: 设置中位数的属性，如线的类型、粗细等\n",
    "\n",
    "meanprops: 设置均值的属性，如点的大小，颜色等；\n",
    "\n",
    "capprops: 设置箱线图顶端和末端线条的属性，如颜色、粗细等；\n",
    "\n",
    "whiskerprops: 设置须的属性，如颜色、粗细、线的类型等"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 美国电影评分的箱线图 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = df[df.产地 == \"美国\"][\"评分\"]\n",
    "\n",
    "plt.figure(figsize = (10,6))\n",
    "plt.boxplot(data,whis = 2,flierprops = {\"marker\":'o',\"markerfacecolor\":\"r\",\"color\":'k'}\n",
    "           ,patch_artist = True, boxprops = {\"color\":'k',\"facecolor\":\"#66ccff\"})\n",
    "plt.title(\"美国电影评分\",fontsize = 20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 多组数据箱线图 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data1 = df[df.产地 == \"中国大陆\"][\"评分\"]\n",
    "data2 = df[df.产地 == \"日本\"][\"评分\"]\n",
    "data3 = df[df.产地 == \"中国香港\"][\"评分\"]\n",
    "data4 = df[df.产地 == \"英国\"][\"评分\"]\n",
    "data5 = df[df.产地 == \"法国\"][\"评分\"]\n",
    "\n",
    "plt.figure(figsize = (12,8))\n",
    "plt.boxplot([data1,data2,data3,data4,data5],labels = [\"中国大陆\",\"日本\",\"中国香港\",\"英国\",\"法国\"],\n",
    "           whis = 2,flierprops = {\"marker\":'o',\"markerfacecolor\":\"r\",\"color\":'k'}\n",
    "           ,patch_artist = True, boxprops = {\"color\":'k',\"facecolor\":\"#66ccff\"},\n",
    "           vert = False)\n",
    "\n",
    "ax = plt.gca() #获取当时的坐标系\n",
    "ax.patch.set_facecolor(\"gray\") #设置坐标系背景颜色\n",
    "ax.patch.set_alpha(0.3) #设置背景透明度\n",
    "\n",
    "plt.title(\"电影评分箱线图\",fontsize = 20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 通过vert属性可以把图旋转过来"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7.相关系数矩阵图--热力图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>投票人数</th>\n",
       "      <th>评分</th>\n",
       "      <th>时长</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>692795</td>\n",
       "      <td>9.6</td>\n",
       "      <td>142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>42995</td>\n",
       "      <td>9.5</td>\n",
       "      <td>116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>327855</td>\n",
       "      <td>9.5</td>\n",
       "      <td>116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>580897</td>\n",
       "      <td>9.4</td>\n",
       "      <td>142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>478523</td>\n",
       "      <td>9.4</td>\n",
       "      <td>171</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     投票人数   评分   时长\n",
       "0  692795  9.6  142\n",
       "1   42995  9.5  116\n",
       "2  327855  9.5  116\n",
       "3  580897  9.4  142\n",
       "4  478523  9.4  171"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = df[[\"投票人数\",\"评分\",\"时长\"]]\n",
    "data[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### pandas本身也封装了画图函数 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 我们可以画出各个属性之间的散点图，对角线是分布图 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%pylab inline \n",
    "#魔术命令，让图像直接展示在notebook里面\n",
    "result = pd.plotting.scatter_matrix(data[::100],diagonal = \"kde\",color = 'k',alpha = 0.3,figsize = (10,10)) \n",
    "#diagonal = hist:对角线上显示的是数据集各个特征的直方图/kde:数据集各个特征的核密度估计"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 现在我们来画电影时长，投票人数，评分的一个相关系数矩阵图"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "seaborn是一个精简的python库，可以创建具有统计意义的图表，能理解pandas的DataFrame类型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "corr = data.corr() #获取相关系数\n",
    "corr = abs(corr) #取绝对值\n",
    "\n",
    "fig = plt.figure(figsize = (10,8))\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "ax = sns.heatmap(corr,vmax = 1,vmin = 0,annot = True,annot_kws = {\"size\":13,\"weight\":\"bold\"},linewidths = 0.05)\n",
    "\n",
    "plt.xticks(fontsize = 15)\n",
    "plt.yticks(fontsize = 15)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 参数详解 \n",
    "\n",
    "```python\n",
    "sns.heatmap(data,vmin=None,vmax=None,cmap=None,center=None,robust=False,annot=None,fmt='.2g',annot_kws=None,linewidths=0,linecolor='white',cbar=True,cbar_kws=None,cbar_ax=None,square=False,xticklabels='auto',yticklabels='auto',mask=None,ax=None,**kwargs,)\n",
    "```\n",
    "\n",
    "（ 1 ）热力图输入数据参数：\n",
    "\n",
    "data:矩阵数据集，可以是numpy的数组（array），也可以是pandas的DataFrame。如果是DataFrame，则df的index/column信息会分别对应到heatmap的columns和rows，即pt.index是热力图的行标，pt.columns是热力图的列标。\n",
    "\n",
    "（ 2 ）热力图矩阵块颜色参数：\n",
    "\n",
    "vmax,vmin:分别是热力图的颜色取值最大和最小范围，默认是根据data数据表里的取值确定。cmap:从数字到色彩空间的映射，取值是matplotlib包里的colormap名称或颜色对象，或者表示颜色的列表；改参数默认值：根据center参数设定。center:数据表取值有差异时，设置热力图的色彩中心对齐值；通过设置center值，可以调整生成的图像颜色的整体深浅；设置center数据时，如果有数据溢出，则手动设置的vmax、vmin会自动改变。robust:默认取值False，如果是False，且没设定vmin和vmax的值。\n",
    "\n",
    "（ 3 ）热力图矩阵块注释参数：\n",
    "\n",
    "annot(annotate的缩写):默认取值False；如果是True，在热力图每个方格写入数据；如果是矩阵，在热力图每个方格写入该矩阵对应位置数据。fmt:字符串格式代码，矩阵上标识数字的数据格式，比如保留小数点后几位数字。annot_kws:默认取值False；如果是True，设置热力图矩阵上数字的大小颜色字体，matplotlib包text类下的字体设置；\n",
    "\n",
    "（ 4 ）热力图矩阵块之间间隔及间隔线参数：\n",
    "\n",
    "linewidth:定义热力图里“表示两两特征关系的矩阵小块”之间的间隔大小。linecolor:切分热力图上每个矩阵小块的线的颜色，默认值是\"white\"。\n",
    "\n",
    "（ 5 ）热力图颜色刻度条参数：\n",
    "\n",
    "cbar:是否在热力图侧边绘制颜色进度条，默认值是True。cbar_kws:热力图侧边绘制颜色刻度条时，相关字体设置，默认值是None。cbar_ax：热力图侧边绘制颜色刻度条时，刻度条位置设置，默认值是None\n",
    "\n",
    "（ 6 ）\n",
    "\n",
    "square:设置热力图矩阵小块形状，默认值是False。xticklabels,yticklabels:xticklabels控制每列标签名的输出；yticklabels控制每行标签名的输出。默认值是auto。如果是True，则以DataFrame的列名作为标签名。如果是False，则不添加行标签名。如果是列表，则标签名改为列表中给的内容。如果是整数K，则在图上每隔K个标签进行一次标注。"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}